Most recent approaches rely on convolutional neural networks (CNNs).
In [1]}, the work in [2]} is extended by adding a CNN to describe the posterior density of an object pose.
A combination of using a CNN for object segmentation and geometry-based pose estimation is proposed in [3]}.
PoseCNN [4]} uses a similar two-stage network, in which the first stage extracts feature maps from RGB input and the second stage uses the generated maps for object segmentation, 3D translation estimation and 3D rotation regression in quaternion format.
Depth data and ICP are used for pose refinement.
Jafari et al. [5]} propose a three-stage, instance-aware approach for 6D object pose estimation.
An instance segmentation network is first applied, followed by an encoder-decoder network which estimates the 3D object coordinates for each segment.
The 6D pose is recovered with a geometric pose optimization step similar to [2]}.
The approaches [1]}, [5]}, [4]} do not directly use CNN to predict the pose.
Instead, they provide segmentation and other intermediate information, which are used to infer the object pose.
